Predict Suicide Warning Signs

Healthcare Public Sector Clinical Improve Health Outcomes Executive Summary
Provide a supplementary assessment that helps prevent suicides and save lives by predicting ahead of time who is likely to commit suicide.
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Overview

Business Problem

The CDC reports that suicide is the 10th leading cause of death in the United States. For those who served in the military, suicide has an even more tragic impact where it is the second leading cause of death. 16.8 veterans die due to suicide every day, as shared by the US Department of Veteran Affairs. While suicide may sometimes appear as if they arose out of nowhere, 90% of the people who commit suicide had a diagnosable mental health condition that could have been treated. In particular, veterans who are more at risk of suicide, compared to the regular individual, may show signs of post-traumatic stress disorders from their time in deployment. Unfortunately, while efforts from government and healthcare institutions to create safe spaces for suicidal individuals to be treated have had some impact on reducing suicides, they are limited by their reactive nature. By then, it is frequently too late as their mental health has deteriorated for long periods of time without receiving the care and help they need.

Intelligent Solution

AI can provide a supplementary assessment that helps prevent suicides and save lives by predicting ahead of time who is likely to commit suicide. In discovering how AI can help the Federal Government reduce the number of warfighters who commit suicide, early results have shown that AI can predict the likelihood a warfighter is at risk of suicide with an overall accuracy of 74%. AI also revealed the reasons behind why warfighters were at risk, showing that thirty-five percent of warfighters who consumed an anxiolytic prescription in the previous six months attempted or committed suicide. As AI produces explainability in its predictions, it allows the government and healthcare workers to understand how they can potentially reduce the likelihood of suicide for each individual at risk, providing personalized mitigation approaches depending on the individual’s background and drivers.

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